Upper extremity amputations can cause a great deal of functional impairment in individuals living with major upper limb loss. While there has been a great deal of recent development in the mechanical design of prosthetic arms, a highly articulated limb is of little use if its movements are not well coordinated or if it is difficult to operate. Electromyographic (EMG) signals have proven to be effective command sources for control of conventional externally-powered upper limb prostheses. These commercially available prosthetic systems use a relatively simple scheme, whereby the amplitude of EMG signals recorded from two sites beneath the socket are used to actuate one of the motors embedded in the prosthesis. This allows only a single degree of freedom to be operated at a time, and requires some type of a mode switch to transition between operating the various joints of the system. Even considering the long history of statistical pattern classification for myoelectric control studied in the research arena, function is usually still limited to a single degree of freedom at a time. Since normal human hand function has coordinated, simultaneous movement of the fingers, thumb, and wrist, this sequential control method can be frustratingly slow due to the significant cognitive burden; this has resulted in the majority of amputees choosing to not use their prostheses over the long term. Going forward, without an improved user interface the overall compliance rate may remain low even with the considerable mechanical advances in prosthetic limbs. One of the long-term goals of our group is to develop more functional myoelectric prostheses for amputees by incorporating a fully implanted EMG recording and telemetry system for use in prosthesis control. This proposal specifically addresses the design, testing, and functional evaluation of EMG-based controllers that use information from muscles in the residual limb to identify the overall, multi-joint motion intent of the user. The overarching hypothesis is that by using 4-8 intramuscular EMG recordings and a pattern detection algorithm, it will be possible to allow multiple joints of a myoelectric prosthesis to be controlled in a highly coordinated and simultaneous fashion. Intramuscular EMG and hand kinematic data will be recorded as subjects perform a variety of movements that require different grasp patterns. Artificial neural networks are proposed as a method for decoding the movement trajectories of the fingers, wrist, and thumb from temporal patterns in the EMG signals. A direct assessment of the functional improvements facilitated by the proposed control scheme will be performed via virtual reality simulations of realistic tasks. By creating a more transparent and effortless control interface for the user, improved functional outcomes and increased acceptance rates of prosthetic limbs should be achievable.